MHC Class I Ligands of Rhesus Macaque Killer Cell Ig-like Receptors.

Definition of MHC class I ligands of rhesus macaque killer cell Ig-like receptors (KIRs) is fundamental to NK cell biology in this species as an animal model for infectious diseases, reproductive biology, and transplantation. To provide a more complete foundation for studying NK cell responses, rhesus macaque KIRs representing common allotypes of lineage II KIR genes were tested for interactions with MHC class I molecules representing diverse Macaca mulatta (Mamu)-A, -B, -E, -F, -I, and -AG alleles. KIR-MHC class I interactions were identified by coincubating reporter cell lines bearing chimeric KIR-CD3ζ receptors with target cells expressing individual MHC class I molecules and were corroborated by staining with KIR IgG-Fc fusion proteins. Ligands for 12 KIRs of previously unknown specificity were identified that fell into three general categories: interactions with multiple Mamu-Bw4 molecules, interactions with Mamu-A-related molecules, including allotypes of Mamu-AG and the hybrid Mamu-B*045:03 molecule, or interactions with Mamu-A1*012:01. Whereas most KIRs found to interact with Mamu-Bw4 are inhibitory, most of the KIRs that interact with Mamu-AG are activating. The KIRs that recognize Mamu-A1*012:01 belong to a phylogenetically distinct group of macaque KIRs with a 3-aa deletion in the D0 domain that is also present in human KIR3DL1/S1 and KIR3DL2. This study more than doubles the number of rhesus macaque KIRs with defined MHC class I ligands and identifies interactions with Mamu-AG, -B*045, and -A1*012. These findings support overlapping, but nonredundant, patterns of ligand recognition that reflect extensive functional diversification of these receptors.

KIR3DL05 and KIR3DS02 Recognition of a Nonclassical MHC Class I Molecule in the Rhesus Macaque Implicated in Pregnancy Success.

Knowledge of the MHC class I ligands of rhesus macaque killer-cell Ig-like receptors (KIRs) is fundamental to understanding the role of natural killer (NK) cells in this species as a nonhuman primate model for infectious diseases, transplantation and reproductive biology. We previously identified Mamu-AG as a ligand for KIR3DL05. Mamu-AG is a nonclassical MHC class I molecule that is expressed at the maternal-fetal interface of the placenta in rhesus macaques similar to HLA-G in humans. Although Mamu-AG and HLA-G share similar molecular features, including limited polymorphism and a short cytoplasmic tail, Mamu-AG is considerably more polymorphic. To determine which allotypes of Mamu-AG serve as ligands for KIR3DL05, we tested reporter cell lines expressing five different alleles of KIR3DL05 (KIR3DL05*001, KIR3DL05*004, KIR3DL05*005, KIR3DL05*008 and KIR3DL05*X) for responses to target cells expressing eight different alleles of Mamu-AG. All five allotypes of KIR3DL05 responded to Mamu-AG2*01:01, two exhibited dominant responses to Mamu-AG1*05:01, and three had low but detectable responses to Mamu-AG3*03:01, -AG3*03:02, -AG3*03:03 and -AG3*03:04. Since KIR3DL05*X is the product of recombination between KIR3DL05 and KIR3DS02, we also tested an allotype of KIR3DS02 (KIR3DS02*004) and found that this activating KIR also recognizes Mamu-AG2*01:01. Additional analysis of Mamu-AG variants with single amino acid substitutions identified residues in the α1-domain essential for recognition by KIR3DL05. These results reveal variation in KIR3DL05 and KIR3DS02 responses to Mamu-AG and define Mamu-AG polymorphisms that differentially affect KIR recognition.

MASH Explorer: A Universal Software Environment for Top-Down Proteomics.

Top-down mass spectrometry (MS)-based proteomics enable a comprehensive analysis of proteoforms with molecular specificity to achieve a proteome-wide understanding of protein functions. However, the lack of a universal software for top-down proteomics is becoming increasingly recognized as a major barrier, especially for newcomers. Here, we have developed MASH Explorer, a universal, comprehensive, and user-friendly software environment for top-down proteomics. MASH Explorer integrates multiple spectral deconvolution and database search algorithms into a single, universal platform which can process top-down proteomics data from various vendor formats, for the first time. It addresses the urgent need in the rapidly growing top-down proteomics community and is freely available to all users worldwide. With the critical need and tremendous support from the community, we envision that this MASH Explorer software package will play an integral role in advancing top-down proteomics to realize its full potential for biomedical research.

Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy.

Top-down mass spectrometry (MS) is a powerful tool for the identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. However, the complex data set generated from top-down MS experiments requires multiple sequential data processing steps to successfully interpret the data for identifying and characterizing proteoforms. One critical step is the deconvolution of the complex isotopic distribution that arises from naturally occurring isotopes. Multiple algorithms are currently available to deconvolute top-down mass spectra, resulting in different deconvoluted peak lists with varied accuracy compared to true positive annotations. In this study, we have designed a machine learning strategy that can process and combine the peak lists from different deconvolution results. By optimizing clustering results, deconvolution results from THRASH, TopFD, MS-Deconv, and SNAP algorithms were combined into consensus peak lists at various thresholds using either a simple voting ensemble method or a random forest machine learning algorithm. For the random forest algorithm, which had better predictive performance, the consensus peak lists on average could achieve a recall value (true positive rate) of 0.60 and a precision value (positive predictive value) of 0.78. It outperforms the single best algorithm, which achieved a recall value of only 0.47 and a precision value of 0.58. This machine learning strategy enhanced the accuracy and confidence in protein identification during database searches by accelerating the detection of true positive peaks while filtering out false positive peaks. Thus, this method shows promise in enhancing proteoform identification and characterization for high-throughput data analysis in top-down proteomics.